6D Robotic OCT Scanning of Curved Tissue Surfaces

• URL: http://arxiv.org/abs/2603.22012v1
• Date: Mon, 23 Mar 2026 14:24:11 GMT
• Title: 6D Robotic OCT Scanning of Curved Tissue Surfaces
• Abstract summary: We propose a marker for full six-dimensional hand-eye calibration of a robot mounted OCT probe.<n>We show that the calibration results in highly repeatable estimates of the transformation.<n>We also demonstrate that the proposed calibration allows for consistent scanning of large, curved tissue surfaces.
• Score: 41.08297278009004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Optical coherence tomography (OCT) is a non-invasive volumetric imaging modality with high spatial and temporal resolution. For imaging larger tissue structures, OCT probes need to be moved to scan the respective area. For handheld scanning, stitching of the acquired OCT volumes requires overlap to register the images. For robotic scanning and stitching, a typical approach is to restrict the motion to translations, as this avoids a full hand-eye calibration, which is complicated by the small field of view of most OCT probes. However, stitching by registration or by translational scanning are limited when curved tissue surfaces need to be scanned. We propose a marker for full six-dimensional hand-eye calibration of a robot mounted OCT probe. We show that the calibration results in highly repeatable estimates of the transformation. Moreover, we evaluate robotic scanning of two phantom surfaces to demonstrate that the proposed calibration allows for consistent scanning of large, curved tissue surfaces. As the proposed approach is not relying on image registration, it does not suffer from a potential accumulation of errors along a scan path. We also illustrate the improvement compared to conventional 3D-translational robotic scanning.

Related papers

• Rendering Novel Views of MRI Using 3D Gaussian Splatting [54.6286495315908]
  We adapt 3D Gaussian Splatting to form a reconstruction starting from sparse anisotropic MRIs, and imaging planes aligned with the anatomy relevant for clinical evaluation are then sampled and rendered.<n>The novel view plane is optimal for diagnostic radiological grading of the target anatomy, whereas the original MRI is not.
  arXiv  Detail & Related papers  (2026-06-24T18:00:03Z)
• Resolution-Independent Neural Operators for Multi-Rate Sparse-View CT [67.14700058302016]
  Deep learning methods achieve high-fidelity reconstructions but often overfit to a fixed acquisition setup.<n>We propose Computed Tomography neural Operator (CTO), a unified CT reconstruction framework that extends to continuous function space.<n>CTO enables consistent multi-sampling-rate and cross-resolution performance, with on average >4dB PSNR gain over CNNs.
  arXiv  Detail & Related papers  (2025-12-13T08:31:46Z)
• Accelerating 3D Photoacoustic Computed Tomography with End-to-End Physics-Aware Neural Operators [74.65171736966131]
  Photoacoustic computed tomography (PACT) combines optical contrast with ultrasonic resolution, achieving deep-tissue imaging beyond the optical diffusion limit.<n>Current implementations require dense transducer arrays and prolonged acquisition times, limiting clinical translation.<n>We introduce Pano, an end-to-end physics-aware model that directly learns the inverse acoustic mapping from sensor measurements to volumetric reconstructions.
  arXiv  Detail & Related papers  (2025-09-11T23:12:55Z)
• Super-Resolution on Rotationally Scanned Photoacoustic Microscopy Images Incorporating Scanning Prior [12.947842858489516]
  Photoacoustic Microscopy (PAM) images integrating the advantages of optical contrast and acoustic resolution have been widely used in brain studies.<n>There exists a trade-off between scanning speed and image resolution. Compared with traditional scanning, rotational scanning provides good opportunities for fast PAM imaging by optimizing the scanning mechanism.<n>In this study, we propose a novel and well-performing super-resolution framework for rotational scanning-based PAM imaging.
  arXiv  Detail & Related papers  (2023-12-12T12:41:35Z)
• Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging [65.47834983591957]
  We propose deep learning based neural networks to correct axial and coronal motion artifacts in OCT based on a single scan. The experimental result shows that the proposed method can effectively correct motion artifacts and achieve smaller error than other methods.
  arXiv  Detail & Related papers  (2023-05-27T03:55:19Z)
• A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT [10.550562752812894]
  OCT instruments image by-scanning a focused light spot across the retina, acquiring cross-sectional images to generate data. Patient eye motion during the acquisition poses unique challenges: non-rigid, distorted distortions occur, leading to gaps in data. We present a new distortion model and a corresponding fully-automatic, reference-free optimization strategy for computational robustness.
  arXiv  Detail & Related papers  (2022-09-15T11:48:53Z)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• Disassemblable Fieldwork CT Scanner Using a 3D-printed Calibration Phantom [8.963422124274095]
  We introduce a low-cost disassemblable CT scanner that allows calibration even when trajectory reproduction is not possible. Using 3D-printed in-image calibration phantoms, we compute a projection matrix directly from each captured X-ray projection.
  arXiv  Detail & Related papers  (2020-11-12T22:07:29Z)
• A Learning-based Method for Online Adjustment of C-arm Cone-Beam CT Source Trajectories for Artifact Avoidance [47.345403652324514]
  The reconstruction quality attainable with commercial CBCT devices is insufficient due to metal artifacts in the presence of pedicle screws. We propose to adjust the C-arm CBCT source trajectory during the scan to optimize reconstruction quality with respect to a certain task. We demonstrate that convolutional neural networks trained on realistically simulated data are capable of predicting quality metrics that enable scene-specific adjustments of the CBCT source trajectory.
  arXiv  Detail & Related papers  (2020-08-14T09:23:50Z)
• Deep OCT Angiography Image Generation for Motion Artifact Suppression [8.442020709975015]
  Affected scans emerge as high intensity (white) or missing (black) regions, resulting in lost information. Deep generative model for OCT to OCTA image translation relies on a single intact OCT scan. A U-Net is trained to extract the angiographic information from OCT patches. At inference, a detection algorithm finds outlier OCTA scans based on their surroundings, which are then replaced by the trained network.
  arXiv  Detail & Related papers  (2020-01-08T13:31:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.